With the increasing amount of available data, the need for classification of large data volumes is permanently growing. In order to cope with this challenge, neural classifiers should be adapted to large-scale data. We present here a well scalable extension to the fuzzy Adaptive Resonance Associative Map (ARAM) neural network, which was specially developed for the quick classification of high-dimensional and large data. This extension aims at increasing the classification speed by adding an extra layer for clustering learned prototypes into large clusters. This enables the activation of only one or a few clusters i.e. a small fraction of all prototypes, reducing the classification time significantly. Further we introduce two methods to adapt this extension to a multi-label classification task.

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<rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/38294">
<dcterms:title>Improving scalability of ART neural networks</dcterms:title>
<dc:contributor>Sapozhnikova, Elena</dc:contributor>
<dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-04-04T09:13:26Z</dcterms:available>
<dc:contributor>Benites, Fernando</dc:contributor>
<dcterms:issued>2017-03</dcterms:issued>
<dc:creator>Benites, Fernando</dc:creator>
<bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/38294"/>
<dc:creator>Sapozhnikova, Elena</dc:creator>
<dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-04-04T09:13:26Z</dc:date>
<dcterms:abstract xml:lang="eng">With the increasing amount of available data, the need for classification of large data volumes is permanently growing. In order to cope with this challenge, neural classifiers should be adapted to large-scale data. We present here a well scalable extension to the fuzzy Adaptive Resonance Associative Map (ARAM) neural network, which was specially developed for the quick classification of high-dimensional and large data. This extension aims at increasing the classification speed by adding an extra layer for clustering learned prototypes into large clusters. This enables the activation of only one or a few clusters i.e. a small fraction of all prototypes, reducing the classification time significantly. Further we introduce two methods to adapt this extension to a multi-label classification task.</dcterms:abstract>
<dc:language>eng</dc:language>
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